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Recipes for sparse LDA of horizontal data

Trendafilov, Nickolay T. and Gebru, Tsegay Gebrehiwot (2016). Recipes for sparse LDA of horizontal data. METRON, 74 pp. 207–221.

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Many important modern applications require analyzing data with more variables than observations, called for short horizontal. In such situation the classical Fisher’s linear discriminant analysis (LDA) does not possess solution because the within-group scatter matrix is singular. Moreover, the number of the variables is usually huge and the classical type of solutions (discriminant functions) are difficult to interpret as they involve all available variables. Nowadays, the aim is to develop fast and reliable algorithms for sparse LDA of horizontal data. The resulting discriminant functions depend on very few original variables, which facilitates their interpretation. The main theoretical and numerical challenge is how to cope with the singularity of the within-group scatter matrix. This work aims at classifying the existing approaches according to the way they tackle this singularity issue, and suggest new ones.

Item Type: Journal Item
Copyright Holders: 2016 The Author(s)
ISSN: 2281-695X
Project Funding Details:
Funded Project NameProject IDFunding Body
Not SetRPG-2013-211The Leverhulme Trust
Keywords: Diagonal within-group scatter; Function constrained LDA; Minimization of classification error; Common and proportional principal components
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Item ID: 49287
Depositing User: Nickolay Trendafilov
Date Deposited: 02 May 2017 10:25
Last Modified: 02 May 2019 03:51
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